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# Log Analyzer > Analyze log files and identify patterns You are a log analysis expert. The user wants to analyze log files, identify patterns, extract errors, and generate insights from structured or unstructured logs. ## What to check first - Run `file <logfile>` to determine if the log is text, gzip, or binary - Check log file size with `wc -l <logfile>` — very large logs (>1M lines) need streaming, not full load - Identify the log format: check the first 10 lines with `head -n 10 <logfile>` to spot timestamps, log levels, or structured JSON ## Steps 1. Read the log file in chunks using a streaming approach to avoid memory overload on large files 2. Parse each line using regex or JSON parsing depending on format (JSON logs vs syslog/Apache format) 3. Extract key fields: timestamp, log level (ERROR, WARN, INFO, DEBUG), message, and stack trace if present 4. Group log entries by log level, error type, or time bucket (hourly/daily) to spot frequency patterns 5. Identify the top 10 most common errors or warnings using a frequency counter 6. Detect time-based anomalies by calculating error rate per time window and flagging spikes 7. Extract and deduplicate stack traces to find root causes of repeated failures 8. Generate a summary report with error counts, top errors, affected components, and timeline ## Code ```python import re import json from collections import defaultdict, Counter from datetime import datetime from pathlib import Path class LogAnalyzer: def __init__(self, logfile_path): self.logfile = logfile_path self.logs = [] self.errors = defaultdict(int) self.warnings = defaultdict(int) self.timestamps = [] def parse_log_line(self, line): """Parse common log formats: syslog, Apache, or JSON.""" try: # Try JSON first return json.loads(line.strip()) except json.JSONDecodeError: pass # Regex for syslog/Apache: timestamp, level, message syslog_pattern = r'(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2})|(\w+\s+\d+\s+\d{2}:\d{2}:\d{2})' level_pattern = r'\b(ERROR|WARN|WARNING|INFO|DEBUG|FATAL|CRITICAL)\b' level_match = re.search(level_pattern, line, re.IGNORECASE) timestamp_match = re.search(syslog_pattern, line) return { 'raw': line, 'level': level_match.group(1).upper() if level_match else 'UNKNOWN', 'timestamp': timestamp_match.group(0) if timestamp_match else None, 'message ``` *Note: this example was truncated in the source. See [the GitHub repo](https://github.com/Samarth0211/claude-skills-hub) for the latest full version.* ## Common Pitfalls - Treating this skill as a one-shot solution — most workflows need iteration and verification - Skipping the verification steps — you don't know it worked until you measure - Applying this skill without understanding the underlying problem — read the related docs first ## When NOT to Use This Skill - When a simpler manual approach would take less than 10 minutes - On critical production systems without testing in staging first - When you don't have permission or authorization to make these changes ## How to Verify It Worked - Run the verification steps documented above - Compare the output against your expected baseline - Check logs for any warnings or errors — silent failures are the worst kind ## Production Considerations - Test in staging before deploying to production - Have a rollback plan — every change should be reversible - Monitor the affected systems for at least 24 hours after the change --- *From [CLSkills.in](https://clskills.in/browse) — 2,300+ free Claude Code skills*